Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations118434
Missing cells26666
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.4 MiB
Average record size in memory339.7 B

Variable types

Text8
Numeric18
Categorical5
DateTime6

Alerts

customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
payment_value is highly overall correlated with priceHigh correlation
price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with price and 3 other fieldsHigh correlation
product_width_cm is highly overall correlated with product_length_cm and 1 other fieldsHigh correlation
seller_state is highly overall correlated with seller_zip_code_prefixHigh correlation
seller_zip_code_prefix is highly overall correlated with seller_stateHigh correlation
order_status is highly imbalanced (91.6%)Imbalance
payment_type is highly imbalanced (52.6%)Imbalance
seller_state is highly imbalanced (63.2%)Imbalance
order_delivered_carrier_date has 2074 (1.8%) missing valuesMissing
order_delivered_customer_date has 3397 (2.9%) missing valuesMissing
product_name_lenght has 2528 (2.1%) missing valuesMissing
product_description_lenght has 2528 (2.1%) missing valuesMissing
product_photos_qty has 2528 (2.1%) missing valuesMissing
product_category has 2553 (2.2%) missing valuesMissing
order_purchase_hour has 2910 (2.5%) zerosZeros

Reproduction

Analysis started2024-08-05 15:33:56.166358
Analysis finished2024-08-05 15:35:41.853676
Duration1 minute and 45.69 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct99441
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:42.350671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3789888
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86952 ?
Unique (%)73.4%

Sample

1st row06b8999e2fba1a1fbc88172c00ba8bc7
2nd row18955e83d337fd6b2def6b18a428ac77
3rd row4e7b3e00288586ebd08712fdd0374a03
4th rowb2b6027bc5c5109e529d4dc6358b12c3
5th row4f2d8ab171c80ec8364f7c12e35b23ad
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
b246eeed30b362c09d867b9e598bee51 22
 
< 0.1%
5e0f7317756669ff7b384444dbb81fa3 21
 
< 0.1%
Other values (99431) 118139
99.8%
2024-08-05T15:35:42.954842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 237574
 
6.3%
2 237529
 
6.3%
5 237365
 
6.3%
c 237224
 
6.3%
6 237112
 
6.3%
1 237092
 
6.3%
8 236965
 
6.3%
d 236915
 
6.3%
a 236874
 
6.3%
7 236840
 
6.2%
Other values (6) 1418398
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2367827
62.5%
Lowercase Letter 1422061
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 237529
10.0%
5 237365
10.0%
6 237112
10.0%
1 237092
10.0%
8 236965
10.0%
7 236840
10.0%
3 236818
10.0%
9 236542
10.0%
4 235900
10.0%
0 235664
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 237574
16.7%
c 237224
16.7%
d 236915
16.7%
a 236874
16.7%
b 236757
16.6%
e 236717
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2367827
62.5%
Latin 1422061
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 237529
10.0%
5 237365
10.0%
6 237112
10.0%
1 237092
10.0%
8 236965
10.0%
7 236840
10.0%
3 236818
10.0%
9 236542
10.0%
4 235900
10.0%
0 235664
10.0%
Latin
ValueCountFrequency (%)
f 237574
16.7%
c 237224
16.7%
d 236915
16.7%
a 236874
16.7%
b 236757
16.6%
e 236717
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3789888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 237574
 
6.3%
2 237529
 
6.3%
5 237365
 
6.3%
c 237224
 
6.3%
6 237112
 
6.3%
1 237092
 
6.3%
8 236965
 
6.3%
d 236915
 
6.3%
a 236874
 
6.3%
7 236840
 
6.2%
Other values (6) 1418398
37.4%
Distinct96096
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:43.453515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3789888
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81671 ?
Unique (%)69.0%

Sample

1st row861eff4711a542e4b93843c6dd7febb0
2nd row290c77bc529b7ac935b93aa66c333dc3
3rd row060e732b5b29e8181a18229c7b0b2b5e
4th row259dac757896d24d7702b9acbbff3f3c
5th row345ecd01c38d18a9036ed96c73b8d066
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c 24
 
< 0.1%
85963fd37bfd387aa6d915d8a1065486 24
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
2524dcec233c3766f2c2b22f69fd65f4 22
 
< 0.1%
Other values (96086) 118113
99.7%
2024-08-05T15:35:44.076497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 237846
 
6.3%
b 237438
 
6.3%
1 237367
 
6.3%
a 237304
 
6.3%
d 237140
 
6.3%
3 237130
 
6.3%
8 237023
 
6.3%
2 236840
 
6.2%
5 236810
 
6.2%
e 236743
 
6.2%
Other values (6) 1418247
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368540
62.5%
Lowercase Letter 1421348
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 237846
10.0%
1 237367
10.0%
3 237130
10.0%
8 237023
10.0%
2 236840
10.0%
5 236810
10.0%
9 236734
10.0%
0 236475
10.0%
7 236468
10.0%
4 235847
10.0%
Lowercase Letter
ValueCountFrequency (%)
b 237438
16.7%
a 237304
16.7%
d 237140
16.7%
e 236743
16.7%
f 236629
16.6%
c 236094
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2368540
62.5%
Latin 1421348
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 237846
10.0%
1 237367
10.0%
3 237130
10.0%
8 237023
10.0%
2 236840
10.0%
5 236810
10.0%
9 236734
10.0%
0 236475
10.0%
7 236468
10.0%
4 235847
10.0%
Latin
ValueCountFrequency (%)
b 237438
16.7%
a 237304
16.7%
d 237140
16.7%
e 236743
16.7%
f 236629
16.6%
c 236094
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3789888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 237846
 
6.3%
b 237438
 
6.3%
1 237367
 
6.3%
a 237304
 
6.3%
d 237140
 
6.3%
3 237130
 
6.3%
8 237023
 
6.3%
2 236840
 
6.2%
5 236810
 
6.2%
e 236743
 
6.2%
Other values (6) 1418247
37.4%

customer_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct14994
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35034.264
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:44.285304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3275
Q111310
median24240
Q358464.5
95-th percentile90560
Maximum99990
Range98987
Interquartile range (IQR)47154.5

Descriptive statistics

Standard deviation29819.29
Coefficient of variation (CV)0.85114646
Kurtosis-0.78080429
Mean35034.264
Median Absolute Deviation (MAD)16230
Skewness0.78547825
Sum4.1492481 × 109
Variance8.8919007 × 108
MonotonicityNot monotonic
2024-08-05T15:35:44.468826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 161
 
0.1%
22790 155
 
0.1%
22793 154
 
0.1%
24230 138
 
0.1%
22775 129
 
0.1%
35162 124
 
0.1%
29101 119
 
0.1%
11740 111
 
0.1%
13087 106
 
0.1%
38400 106
 
0.1%
Other values (14984) 117131
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 4
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 3
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99990 1
 
< 0.1%
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 2
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%
Distinct4119
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:44.963232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.335622
Min length3

Characters and Unicode

Total characters1224089
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1037 ?
Unique (%)0.9%

Sample

1st rowfranca
2nd rowsao bernardo do campo
3rd rowsao paulo
4th rowmogi das cruzes
5th rowcampinas
ValueCountFrequency (%)
sao 25280
 
12.2%
paulo 18820
 
9.1%
de 11576
 
5.6%
rio 9902
 
4.8%
janeiro 8252
 
4.0%
do 5072
 
2.4%
belo 3346
 
1.6%
horizonte 3300
 
1.6%
brasilia 2483
 
1.2%
porto 1984
 
1.0%
Other values (3285) 117718
56.7%
2024-08-05T15:35:45.635079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 201908
16.5%
o 151033
12.3%
i 93579
 
7.6%
r 90680
 
7.4%
89299
 
7.3%
e 79475
 
6.5%
s 75013
 
6.1%
n 54237
 
4.4%
u 53750
 
4.4%
l 53333
 
4.4%
Other values (21) 281782
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1134240
92.7%
Space Separator 89299
 
7.3%
Dash Punctuation 286
 
< 0.1%
Other Punctuation 262
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 201908
17.8%
o 151033
13.3%
i 93579
 
8.3%
r 90680
 
8.0%
e 79475
 
7.0%
s 75013
 
6.6%
n 54237
 
4.8%
u 53750
 
4.7%
l 53333
 
4.7%
p 44505
 
3.9%
Other values (16) 236727
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
89299
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 286
100.0%
Other Punctuation
ValueCountFrequency (%)
' 262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1134240
92.7%
Common 89849
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 201908
17.8%
o 151033
13.3%
i 93579
 
8.3%
r 90680
 
8.0%
e 79475
 
7.0%
s 75013
 
6.6%
n 54237
 
4.8%
u 53750
 
4.7%
l 53333
 
4.7%
p 44505
 
3.9%
Other values (16) 236727
20.9%
Common
ValueCountFrequency (%)
89299
99.4%
- 286
 
0.3%
' 262
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1224089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 201908
16.5%
o 151033
12.3%
i 93579
 
7.6%
r 90680
 
7.4%
89299
 
7.3%
e 79475
 
6.5%
s 75013
 
6.1%
n 54237
 
4.4%
u 53750
 
4.4%
l 53333
 
4.4%
Other values (21) 281782
23.0%

customer_state
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
SP
49967 
RJ
15420 
MG
13738 
RS
6521 
PR
6017 
Other values (22)
26771 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236868
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 49967
42.2%
RJ 15420
 
13.0%
MG 13738
 
11.6%
RS 6521
 
5.5%
PR 6017
 
5.1%
SC 4328
 
3.7%
BA 4071
 
3.4%
DF 2489
 
2.1%
GO 2443
 
2.1%
ES 2347
 
2.0%
Other values (17) 11093
 
9.4%

Length

2024-08-05T15:35:45.795660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 49967
42.2%
rj 15420
 
13.0%
mg 13738
 
11.6%
rs 6521
 
5.5%
pr 6017
 
5.1%
sc 4328
 
3.7%
ba 4071
 
3.4%
df 2489
 
2.1%
go 2443
 
2.1%
es 2347
 
2.0%
Other values (17) 11093
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 64415
27.2%
P 60302
25.5%
R 28926
12.2%
M 16739
 
7.1%
G 16181
 
6.8%
J 15420
 
6.5%
A 6855
 
2.9%
E 6204
 
2.6%
C 5983
 
2.5%
B 4714
 
2.0%
Other values (7) 11129
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236868
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 64415
27.2%
P 60302
25.5%
R 28926
12.2%
M 16739
 
7.1%
G 16181
 
6.8%
J 15420
 
6.5%
A 6855
 
2.9%
E 6204
 
2.6%
C 5983
 
2.5%
B 4714
 
2.0%
Other values (7) 11129
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 236868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 64415
27.2%
P 60302
25.5%
R 28926
12.2%
M 16739
 
7.1%
G 16181
 
6.8%
J 15420
 
6.5%
A 6855
 
2.9%
E 6204
 
2.6%
C 5983
 
2.5%
B 4714
 
2.0%
Other values (7) 11129
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 64415
27.2%
P 60302
25.5%
R 28926
12.2%
M 16739
 
7.1%
G 16181
 
6.8%
J 15420
 
6.5%
A 6855
 
2.9%
E 6204
 
2.6%
C 5983
 
2.5%
B 4714
 
2.0%
Other values (7) 11129
 
4.7%
Distinct99441
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:46.264133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3789888
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86952 ?
Unique (%)73.4%

Sample

1st row00e7ee1b050b8499577073aeb2a297a1
2nd row29150127e6685892b6eab3eec79f59c7
3rd rowb2059ed67ce144a36e2aa97d2c9e9ad2
4th row951670f92359f4fe4a63112aa7306eba
5th row6b7d50bd145f6fc7f33cebabd7e49d0f
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
285c2e15bebd4ac83635ccc563dc71f4 22
 
< 0.1%
958c6a70e60365b576dd696ad29bbca2 21
 
< 0.1%
Other values (99431) 118139
99.8%
2024-08-05T15:35:46.882978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 237976
 
6.3%
b 237895
 
6.3%
6 237797
 
6.3%
e 237449
 
6.3%
3 237239
 
6.3%
8 237121
 
6.3%
c 237072
 
6.3%
7 237053
 
6.3%
1 237035
 
6.3%
a 236741
 
6.2%
Other values (6) 1416510
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368219
62.5%
Lowercase Letter 1421669
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 237976
10.0%
6 237797
10.0%
3 237239
10.0%
8 237121
10.0%
7 237053
10.0%
1 237035
10.0%
2 236488
10.0%
9 236349
10.0%
0 235705
10.0%
5 235456
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 237895
16.7%
e 237449
16.7%
c 237072
16.7%
a 236741
16.7%
f 236462
16.6%
d 236050
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2368219
62.5%
Latin 1421669
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 237976
10.0%
6 237797
10.0%
3 237239
10.0%
8 237121
10.0%
7 237053
10.0%
1 237035
10.0%
2 236488
10.0%
9 236349
10.0%
0 235705
10.0%
5 235456
9.9%
Latin
ValueCountFrequency (%)
b 237895
16.7%
e 237449
16.7%
c 237072
16.7%
a 236741
16.7%
f 236462
16.6%
d 236050
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3789888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 237976
 
6.3%
b 237895
 
6.3%
6 237797
 
6.3%
e 237449
 
6.3%
3 237239
 
6.3%
8 237121
 
6.3%
c 237072
 
6.3%
7 237053
 
6.3%
1 237035
 
6.3%
a 236741
 
6.2%
Other values (6) 1416510
37.4%

order_status
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
delivered
115038 
shipped
 
1245
canceled
 
745
unavailable
 
650
processing
 
375
Other values (3)
 
381

Length

Max length11
Median length9
Mean length8.9835689
Min length7

Characters and Unicode

Total characters1063960
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 115038
97.1%
shipped 1245
 
1.1%
canceled 745
 
0.6%
unavailable 650
 
0.5%
processing 375
 
0.3%
invoiced 373
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Length

2024-08-05T15:35:47.070140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-05T15:35:47.265753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
delivered 115038
97.1%
shipped 1245
 
1.1%
canceled 745
 
0.6%
unavailable 650
 
0.5%
processing 375
 
0.3%
invoiced 373
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 349260
32.8%
d 232447
21.8%
i 118054
 
11.1%
l 117083
 
11.0%
v 116064
 
10.9%
r 115421
 
10.8%
p 2871
 
0.3%
a 2703
 
0.3%
c 2243
 
0.2%
n 2143
 
0.2%
Other values (7) 5671
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1063960
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 349260
32.8%
d 232447
21.8%
i 118054
 
11.1%
l 117083
 
11.0%
v 116064
 
10.9%
r 115421
 
10.8%
p 2871
 
0.3%
a 2703
 
0.3%
c 2243
 
0.2%
n 2143
 
0.2%
Other values (7) 5671
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1063960
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 349260
32.8%
d 232447
21.8%
i 118054
 
11.1%
l 117083
 
11.0%
v 116064
 
10.9%
r 115421
 
10.8%
p 2871
 
0.3%
a 2703
 
0.3%
c 2243
 
0.2%
n 2143
 
0.2%
Other values (7) 5671
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1063960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 349260
32.8%
d 232447
21.8%
i 118054
 
11.1%
l 117083
 
11.0%
v 116064
 
10.9%
r 115421
 
10.8%
p 2871
 
0.3%
a 2703
 
0.3%
c 2243
 
0.2%
n 2143
 
0.2%
Other values (7) 5671
 
0.5%
Distinct98875
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
Minimum2016-09-04 21:15:19
Maximum2018-10-17 17:30:18
2024-08-05T15:35:47.434443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:47.605871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct90733
Distinct (%)76.7%
Missing176
Missing (%)0.1%
Memory size5.8 MiB
Minimum2016-09-15 12:16:38
Maximum2018-09-03 17:40:06
2024-08-05T15:35:47.792770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:47.954083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct81018
Distinct (%)69.6%
Missing2074
Missing (%)1.8%
Memory size5.8 MiB
Minimum2016-10-08 10:34:01
Maximum2018-09-11 19:48:28
2024-08-05T15:35:48.135365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:48.310635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct95664
Distinct (%)83.2%
Missing3397
Missing (%)2.9%
Memory size5.8 MiB
Minimum2016-10-11 13:46:32
Maximum2018-10-17 13:22:46
2024-08-05T15:35:48.497087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:48.673166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct459
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
Minimum2016-09-30 00:00:00
Maximum2018-11-12 00:00:00
2024-08-05T15:35:48.848602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:49.022461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing830
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.1959202
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:49.184313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69772067
Coefficient of variation (CV)0.58341741
Kurtosis104.20293
Mean1.1959202
Median Absolute Deviation (MAD)0
Skewness7.5710461
Sum140645
Variance0.48681413
MonotonicityNot monotonic
2024-08-05T15:35:49.324209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 103057
87.0%
2 10239
 
8.6%
3 2376
 
2.0%
4 986
 
0.8%
5 469
 
0.4%
6 262
 
0.2%
7 60
 
0.1%
8 36
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 66
 
0.1%
(Missing) 830
 
0.7%
ValueCountFrequency (%)
1 103057
87.0%
2 10239
 
8.6%
3 2376
 
2.0%
4 986
 
0.8%
5 469
 
0.4%
6 262
 
0.2%
7 60
 
0.1%
8 36
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
< 0.1%
19 3
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 5
 
< 0.1%
14 7
< 0.1%
13 8
< 0.1%
12 13
< 0.1%
Distinct32951
Distinct (%)28.0%
Missing830
Missing (%)0.7%
Memory size5.8 MiB
2024-08-05T15:35:49.698577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3763328
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17444 ?
Unique (%)14.8%

Sample

1st rowa9516a079e37a9c9c36b9b78b10169e8
2nd row4aa6014eceb682077f9dc4bffebc05b0
3rd rowbd07b66896d6f1494f5b86251848ced7
4th rowa5647c44af977b148e0a3a4751a09e2e
5th row9391a573abe00141c56e38d84d7d5b3b
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 536
 
0.5%
99a4788cb24856965c36a24e339b6058 525
 
0.4%
422879e10f46682990de24d770e7f83d 505
 
0.4%
389d119b48cf3043d311335e499d9c6b 406
 
0.3%
368c6c730842d78016ad823897a372db 395
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 357
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 327
 
0.3%
154e7e31ebfa092203795c972e5804a6 283
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7 278
 
0.2%
Other values (32941) 113603
96.6%
2024-08-05T15:35:50.205589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 241732
 
6.4%
9 239628
 
6.4%
e 237452
 
6.3%
8 236819
 
6.3%
7 236771
 
6.3%
4 236040
 
6.3%
a 235924
 
6.3%
c 234931
 
6.2%
0 234839
 
6.2%
2 234705
 
6.2%
Other values (6) 1394487
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2361683
62.8%
Lowercase Letter 1401645
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 241732
10.2%
9 239628
10.1%
8 236819
10.0%
7 236771
10.0%
4 236040
10.0%
0 234839
9.9%
2 234705
9.9%
6 234263
9.9%
5 234216
9.9%
1 232670
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 237452
16.9%
a 235924
16.8%
c 234931
16.8%
b 233596
16.7%
d 231370
16.5%
f 228372
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2361683
62.8%
Latin 1401645
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 241732
10.2%
9 239628
10.1%
8 236819
10.0%
7 236771
10.0%
4 236040
10.0%
0 234839
9.9%
2 234705
9.9%
6 234263
9.9%
5 234216
9.9%
1 232670
9.9%
Latin
ValueCountFrequency (%)
e 237452
16.9%
a 235924
16.8%
c 234931
16.8%
b 233596
16.7%
d 231370
16.5%
f 228372
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3763328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 241732
 
6.4%
9 239628
 
6.4%
e 237452
 
6.3%
8 236819
 
6.3%
7 236771
 
6.3%
4 236040
 
6.3%
a 235924
 
6.3%
c 234931
 
6.2%
0 234839
 
6.2%
2 234705
 
6.2%
Other values (6) 1394487
37.1%
Distinct3095
Distinct (%)2.6%
Missing830
Missing (%)0.7%
Memory size5.8 MiB
2024-08-05T15:35:50.581284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3763328
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)0.4%

Sample

1st row7c67e1448b00f6e969d365cea6b010ab
2nd rowb8bc237ba3788b23da09c0f1f3a3288c
3rd row7c67e1448b00f6e969d365cea6b010ab
4th row7c67e1448b00f6e969d365cea6b010ab
5th row4a3ca9315b744ce9f8e9374361493884
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2133
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2122
 
1.8%
1f50f920176fa81dab994f9023523100 2008
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1847
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1639
 
1.4%
955fee9216a65b617aa5c0531780ce60 1528
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1462
 
1.2%
7c67e1448b00f6e969d365cea6b010ab 1452
 
1.2%
7a67c85e85bb2ce8582c35f2203ad736 1240
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1239
 
1.1%
Other values (3085) 100934
85.8%
2024-08-05T15:35:51.053622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 255271
 
6.8%
c 248272
 
6.6%
4 246945
 
6.6%
6 242188
 
6.4%
0 241220
 
6.4%
a 239844
 
6.4%
b 239238
 
6.4%
3 239187
 
6.4%
9 233721
 
6.2%
2 232497
 
6.2%
Other values (6) 1344945
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380420
63.3%
Lowercase Letter 1382908
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 255271
10.7%
4 246945
10.4%
6 242188
10.2%
0 241220
10.1%
3 239187
10.0%
9 233721
9.8%
2 232497
9.8%
8 230272
9.7%
5 229725
9.7%
7 229394
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 248272
18.0%
a 239844
17.3%
b 239238
17.3%
e 221358
16.0%
f 217994
15.8%
d 216202
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2380420
63.3%
Latin 1382908
36.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 255271
10.7%
4 246945
10.4%
6 242188
10.2%
0 241220
10.1%
3 239187
10.0%
9 233721
9.8%
2 232497
9.8%
8 230272
9.7%
5 229725
9.7%
7 229394
9.6%
Latin
ValueCountFrequency (%)
c 248272
18.0%
a 239844
17.3%
b 239238
17.3%
e 221358
16.0%
f 217994
15.8%
d 216202
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3763328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 255271
 
6.8%
c 248272
 
6.6%
4 246945
 
6.6%
6 242188
 
6.4%
0 241220
 
6.4%
a 239844
 
6.4%
b 239238
 
6.4%
3 239187
 
6.4%
9 233721
 
6.2%
2 232497
 
6.2%
Other values (6) 1344945
35.7%
Distinct93318
Distinct (%)79.3%
Missing830
Missing (%)0.7%
Memory size5.8 MiB
Minimum2016-09-19 00:15:34
Maximum2020-04-09 22:35:08
2024-08-05T15:35:51.250123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:51.890007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct5968
Distinct (%)5.1%
Missing830
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean120.82285
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:52.062182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.47737
Coefficient of variation (CV)1.5268417
Kurtosis118.86346
Mean120.82285
Median Absolute Deviation (MAD)42
Skewness7.8857991
Sum14209250
Variance34031.899
MonotonicityNot monotonic
2024-08-05T15:35:52.232700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2606
 
2.2%
69.9 2092
 
1.8%
49.9 2045
 
1.7%
89.9 1623
 
1.4%
99.9 1510
 
1.3%
39.9 1395
 
1.2%
29.9 1378
 
1.2%
19.9 1279
 
1.1%
79.9 1276
 
1.1%
29.99 1221
 
1.0%
Other values (5958) 101179
85.4%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6999
Distinct (%)6.0%
Missing830
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean20.045551
Minimum0
Maximum409.68
Zeros390
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:52.408684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.29
Q321.19
95-th percentile45.3
Maximum409.68
Range409.68
Interquartile range (IQR)8.11

Descriptive statistics

Standard deviation15.861351
Coefficient of variation (CV)0.79126539
Kurtosis57.583093
Mean20.045551
Median Absolute Deviation (MAD)3.63
Skewness5.5429427
Sum2357437
Variance251.58245
MonotonicityNot monotonic
2024-08-05T15:35:52.578956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3831
 
3.2%
7.78 2339
 
2.0%
11.85 1972
 
1.7%
14.1 1971
 
1.7%
18.23 1625
 
1.4%
7.39 1568
 
1.3%
16.11 1194
 
1.0%
15.23 1061
 
0.9%
8.72 964
 
0.8%
16.79 924
 
0.8%
Other values (6989) 100155
84.6%
ValueCountFrequency (%)
0 390
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 9
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.0942068
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:52.727957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7282895
Coefficient of variation (CV)0.66558674
Kurtosis346.64585
Mean1.0942068
Median Absolute Deviation (MAD)0
Skewness15.881705
Sum129588
Variance0.5304056
MonotonicityNot monotonic
2024-08-05T15:35:52.860072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 113332
95.7%
2 3397
 
2.9%
3 651
 
0.5%
4 318
 
0.3%
5 191
 
0.2%
6 134
 
0.1%
7 92
 
0.1%
8 61
 
0.1%
9 50
 
< 0.1%
10 41
 
< 0.1%
Other values (19) 164
 
0.1%
ValueCountFrequency (%)
1 113332
95.7%
2 3397
 
2.9%
3 651
 
0.5%
4 318
 
0.3%
5 191
 
0.2%
6 134
 
0.1%
7 92
 
0.1%
8 61
 
0.1%
9 50
 
< 0.1%
10 41
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
< 0.1%
20 6
< 0.1%

payment_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size5.8 MiB
credit_card
87286 
boleto
23037 
voucher
 
6407
debit_card
 
1698
not_defined
 
3

Length

Max length11
Median length11
Mean length9.7966749
Min length6

Characters and Unicode

Total characters1160230
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcredit_card
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 87286
73.7%
boleto 23037
 
19.5%
voucher 6407
 
5.4%
debit_card 1698
 
1.4%
not_defined 3
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2024-08-05T15:35:53.005336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-05T15:35:53.146329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 87286
73.7%
boleto 23037
 
19.5%
voucher 6407
 
5.4%
debit_card 1698
 
1.4%
not_defined 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 182677
15.7%
r 182677
15.7%
d 177974
15.3%
e 118434
10.2%
t 112024
9.7%
i 88987
7.7%
_ 88987
7.7%
a 88984
7.7%
o 52484
 
4.5%
b 24735
 
2.1%
Other values (6) 42267
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1071243
92.3%
Connector Punctuation 88987
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 182677
17.1%
r 182677
17.1%
d 177974
16.6%
e 118434
11.1%
t 112024
10.5%
i 88987
8.3%
a 88984
8.3%
o 52484
 
4.9%
b 24735
 
2.3%
l 23037
 
2.2%
Other values (5) 19230
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 88987
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1071243
92.3%
Common 88987
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 182677
17.1%
r 182677
17.1%
d 177974
16.6%
e 118434
11.1%
t 112024
10.5%
i 88987
8.3%
a 88984
8.3%
o 52484
 
4.9%
b 24735
 
2.3%
l 23037
 
2.2%
Other values (5) 19230
 
1.8%
Common
ValueCountFrequency (%)
_ 88987
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1160230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 182677
15.7%
r 182677
15.7%
d 177974
15.3%
e 118434
10.2%
t 112024
9.7%
i 88987
7.7%
_ 88987
7.7%
a 88984
7.7%
o 52484
 
4.5%
b 24735
 
2.1%
Other values (6) 42267
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9373981
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:53.280792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7741644
Coefficient of variation (CV)0.94442914
Kurtosis2.5218464
Mean2.9373981
Median Absolute Deviation (MAD)1
Skewness1.6227641
Sum347879
Variance7.6959881
MonotonicityNot monotonic
2024-08-05T15:35:53.418015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 59117
49.9%
2 13794
 
11.6%
3 11814
 
10.0%
4 8027
 
6.8%
10 6899
 
5.8%
5 6056
 
5.1%
8 5077
 
4.3%
6 4638
 
3.9%
7 1839
 
1.6%
9 735
 
0.6%
Other values (14) 435
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 59117
49.9%
2 13794
 
11.6%
3 11814
 
10.0%
4 8027
 
6.8%
5 6056
 
5.1%
6 4638
 
3.9%
7 1839
 
1.6%
8 5077
 
4.3%
9 735
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 5
 
< 0.1%
20 21
 
< 0.1%
18 38
< 0.1%
17 8
 
< 0.1%
16 7
 
< 0.1%
15 92
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

HIGH CORRELATION 

Distinct29077
Distinct (%)24.6%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean172.84939
Minimum0
Maximum13664.08
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:53.574811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.18
Q160.86
median108.2
Q3189.245
95-th percentile515.94
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.385

Descriptive statistics

Standard deviation268.25983
Coefficient of variation (CV)1.5519859
Kurtosis499.70102
Mean172.84939
Median Absolute Deviation (MAD)56.63
Skewness13.969151
Sum20470727
Variance71963.337
MonotonicityNot monotonic
2024-08-05T15:35:53.746300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 351
 
0.3%
100 300
 
0.3%
20 286
 
0.2%
77.57 250
 
0.2%
35 166
 
0.1%
73.34 160
 
0.1%
30 139
 
0.1%
116.94 131
 
0.1%
56.78 123
 
0.1%
65 119
 
0.1%
Other values (29067) 116406
98.3%
ValueCountFrequency (%)
0 9
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 2
 
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6922.21 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%

product_name_lenght
Real number (ℝ)

MISSING 

Distinct66
Distinct (%)0.1%
Missing2528
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean48.767208
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:53.924357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.035162
Coefficient of variation (CV)0.20577685
Kurtosis0.15160625
Mean48.767208
Median Absolute Deviation (MAD)6
Skewness-0.90552833
Sum5652412
Variance100.70448
MonotonicityNot monotonic
2024-08-05T15:35:54.091229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8630
 
7.3%
60 8030
 
6.8%
56 6797
 
5.7%
58 6776
 
5.7%
57 6271
 
5.3%
55 5797
 
4.9%
54 5489
 
4.6%
53 4338
 
3.7%
52 4296
 
3.6%
49 3665
 
3.1%
Other values (56) 55817
47.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 15
 
< 0.1%
10 9
 
< 0.1%
11 11
 
< 0.1%
12 38
< 0.1%
13 26
< 0.1%
14 47
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 2
 
< 0.1%
66 1
 
< 0.1%
64 171
 
0.1%
63 1344
1.1%
62 163
 
0.1%
61 241
 
0.2%

product_description_lenght
Real number (ℝ)

MISSING 

Distinct2960
Distinct (%)2.6%
Missing2528
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean786.9573
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:54.282331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1347
median601
Q3985
95-th percentile2126
Maximum3992
Range3988
Interquartile range (IQR)638

Descriptive statistics

Standard deviation653.02901
Coefficient of variation (CV)0.82981504
Kurtosis4.9175956
Mean786.9573
Median Absolute Deviation (MAD)296
Skewness2.0100931
Sum91213073
Variance426446.89
MonotonicityNot monotonic
2024-08-05T15:35:54.456922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 710
 
0.6%
1893 667
 
0.6%
348 644
 
0.5%
903 594
 
0.5%
245 584
 
0.5%
492 577
 
0.5%
366 534
 
0.5%
236 516
 
0.4%
340 484
 
0.4%
919 441
 
0.4%
Other values (2950) 110155
93.0%
(Missing) 2528
 
2.1%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 4
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing2528
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.2078495
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:54.622676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7195193
Coefficient of variation (CV)0.77882088
Kurtosis4.8052032
Mean2.2078495
Median Absolute Deviation (MAD)0
Skewness1.9057908
Sum255903
Variance2.9567465
MonotonicityNot monotonic
2024-08-05T15:35:54.746016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 58542
49.4%
2 22896
 
19.3%
3 12920
 
10.9%
4 8830
 
7.5%
5 5585
 
4.7%
6 3935
 
3.3%
7 1556
 
1.3%
8 772
 
0.7%
10 353
 
0.3%
9 318
 
0.3%
Other values (9) 199
 
0.2%
(Missing) 2528
 
2.1%
ValueCountFrequency (%)
1 58542
49.4%
2 22896
 
19.3%
3 12920
 
10.9%
4 8830
 
7.5%
5 5585
 
4.7%
6 3935
 
3.3%
7 1556
 
1.3%
8 772
 
0.7%
9 318
 
0.3%
10 353
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 60
 
0.1%
11 73
 
0.1%
10 353
0.3%

product_weight_g
Real number (ℝ)

HIGH CORRELATION 

Distinct2204
Distinct (%)1.9%
Missing850
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2114.2811
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:54.924724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3788.754
Coefficient of variation (CV)1.7919822
Kurtosis16.00722
Mean2114.2811
Median Absolute Deviation (MAD)500
Skewness3.5812411
Sum2.4860562 × 108
Variance14354657
MonotonicityNot monotonic
2024-08-05T15:35:55.104338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7051
 
6.0%
150 5391
 
4.6%
250 4702
 
4.0%
300 4419
 
3.7%
400 3750
 
3.2%
100 3628
 
3.1%
350 3264
 
2.8%
500 2836
 
2.4%
600 2822
 
2.4%
700 2131
 
1.8%
Other values (2194) 77590
65.5%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 988
0.8%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 302
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)0.1%
Missing850
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean30.256872
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:55.290669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.190874
Coefficient of variation (CV)0.53511393
Kurtosis3.6874379
Mean30.256872
Median Absolute Deviation (MAD)8
Skewness1.7478347
Sum3557724
Variance262.14439
MonotonicityNot monotonic
2024-08-05T15:35:55.461508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 18274
 
15.4%
20 10918
 
9.2%
30 7907
 
6.7%
17 6177
 
5.2%
18 5883
 
5.0%
19 4863
 
4.1%
25 4833
 
4.1%
40 4306
 
3.6%
22 3971
 
3.4%
50 3149
 
2.7%
Other values (89) 47303
39.9%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
 
0.1%
12 41
 
< 0.1%
13 60
 
0.1%
14 138
 
0.1%
15 218
 
0.2%
16 18274
15.4%
ValueCountFrequency (%)
105 334
0.3%
104 35
 
< 0.1%
103 46
 
< 0.1%
102 59
 
< 0.1%
101 108
 
0.1%
100 426
0.4%
99 36
 
< 0.1%
98 50
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)0.1%
Missing850
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean16.63029
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:55.632270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.45878
Coefficient of variation (CV)0.8092932
Kurtosis7.2720955
Mean16.63029
Median Absolute Deviation (MAD)6
Skewness2.2376777
Sum1955456
Variance181.13877
MonotonicityNot monotonic
2024-08-05T15:35:55.806000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10296
 
8.7%
20 6891
 
5.8%
15 6839
 
5.8%
12 6486
 
5.5%
11 6378
 
5.4%
2 5213
 
4.4%
4 4881
 
4.1%
8 4826
 
4.1%
16 4742
 
4.0%
5 4741
 
4.0%
Other values (92) 56291
47.5%
ValueCountFrequency (%)
2 5213
4.4%
3 2809
 
2.4%
4 4881
4.1%
5 4741
4.0%
6 3553
 
3.0%
7 4376
3.7%
8 4826
4.1%
9 3371
 
2.8%
10 10296
8.7%
11 6378
5.4%
ValueCountFrequency (%)
105 138
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 43
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.1%
Missing850
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean23.068394
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:55.983138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.748372
Coefficient of variation (CV)0.50928437
Kurtosis4.5712383
Mean23.068394
Median Absolute Deviation (MAD)6
Skewness1.7102323
Sum2712474
Variance138.02426
MonotonicityNot monotonic
2024-08-05T15:35:56.147410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12650
 
10.7%
11 11083
 
9.4%
15 9316
 
7.9%
16 8775
 
7.4%
30 7988
 
6.7%
12 5684
 
4.8%
13 5456
 
4.6%
14 4807
 
4.1%
18 4160
 
3.5%
40 4130
 
3.5%
Other values (85) 43535
36.8%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
 
< 0.1%
10 83
 
0.1%
11 11083
9.4%
12 5684
4.8%
13 5456
4.6%
14 4807
4.1%
15 9316
7.9%
ValueCountFrequency (%)
118 8
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 43
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%

product_category
Text

MISSING 

Distinct71
Distinct (%)0.1%
Missing2553
Missing (%)2.2%
Memory size5.8 MiB
2024-08-05T15:35:56.487931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length12.981162
Min length3

Characters and Unicode

Total characters1504270
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoffice_furniture
2nd rowhousewares
3rd rowoffice_furniture
4th rowoffice_furniture
5th rowhome_confort
ValueCountFrequency (%)
bed_bath_table 11823
 
10.2%
health_beauty 9975
 
8.6%
sports_leisure 8945
 
7.7%
furniture_decor 8744
 
7.5%
computers_accessories 8082
 
7.0%
housewares 7355
 
6.3%
watches_gifts 6201
 
5.4%
telephony 4721
 
4.1%
garden_tools 4574
 
3.9%
auto 4379
 
3.8%
Other values (61) 41082
35.5%
2024-08-05T15:35:57.016292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 184021
12.2%
s 141292
 
9.4%
t 132515
 
8.8%
o 111308
 
7.4%
r 105097
 
7.0%
a 101696
 
6.8%
_ 101427
 
6.7%
u 77730
 
5.2%
c 72030
 
4.8%
i 62939
 
4.2%
Other values (15) 414215
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1402544
93.2%
Connector Punctuation 101427
 
6.7%
Decimal Number 299
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 184021
13.1%
s 141292
 
10.1%
t 132515
 
9.4%
o 111308
 
7.9%
r 105097
 
7.5%
a 101696
 
7.3%
u 77730
 
5.5%
c 72030
 
5.1%
i 62939
 
4.5%
h 59263
 
4.2%
Other values (13) 354653
25.3%
Connector Punctuation
ValueCountFrequency (%)
_ 101427
100.0%
Decimal Number
ValueCountFrequency (%)
2 299
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1402544
93.2%
Common 101726
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 184021
13.1%
s 141292
 
10.1%
t 132515
 
9.4%
o 111308
 
7.9%
r 105097
 
7.5%
a 101696
 
7.3%
u 77730
 
5.5%
c 72030
 
5.1%
i 62939
 
4.5%
h 59263
 
4.2%
Other values (13) 354653
25.3%
Common
ValueCountFrequency (%)
_ 101427
99.7%
2 299
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1504270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 184021
12.2%
s 141292
 
9.4%
t 132515
 
8.8%
o 111308
 
7.4%
r 105097
 
7.0%
a 101696
 
6.8%
_ 101427
 
6.7%
u 77730
 
5.2%
c 72030
 
4.8%
i 62939
 
4.2%
Other values (15) 414215
27.5%

seller_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct2246
Distinct (%)1.9%
Missing830
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean24442.886
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:57.199370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2972
Q16429
median13660
Q328035
95-th percentile88316
Maximum99730
Range98729
Interquartile range (IQR)21606

Descriptive statistics

Standard deviation27573.803
Coefficient of variation (CV)1.1280911
Kurtosis0.93603424
Mean24442.886
Median Absolute Deviation (MAD)8130
Skewness1.5556839
Sum2.8745812 × 109
Variance7.6031461 × 108
MonotonicityNot monotonic
2024-08-05T15:35:57.378230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14940 8242
 
7.0%
5849 2137
 
1.8%
15025 2089
 
1.8%
9015 1853
 
1.6%
13405 1655
 
1.4%
4782 1547
 
1.3%
8577 1545
 
1.3%
3204 1462
 
1.2%
4160 1267
 
1.1%
13232 1254
 
1.1%
Other values (2236) 94553
79.8%
ValueCountFrequency (%)
1001 22
 
< 0.1%
1021 41
 
< 0.1%
1022 5
 
< 0.1%
1023 5
 
< 0.1%
1026 323
0.3%
1031 129
 
0.1%
1035 18
 
< 0.1%
1039 1
 
< 0.1%
1040 24
 
< 0.1%
1041 2
 
< 0.1%
ValueCountFrequency (%)
99730 12
 
< 0.1%
99700 2
 
< 0.1%
99670 1
 
< 0.1%
99500 61
0.1%
99300 2
 
< 0.1%
98975 22
 
< 0.1%
98920 2
 
< 0.1%
98910 13
 
< 0.1%
98803 64
0.1%
98780 4
 
< 0.1%
Distinct611
Distinct (%)0.5%
Missing830
Missing (%)0.7%
Memory size5.8 MiB
2024-08-05T15:35:57.915784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Length

Max length40
Median length31
Mean length10.103466
Min length2

Characters and Unicode

Total characters1188208
Distinct characters41
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowitaquaquecetuba
2nd rowitajai
3rd rowitaquaquecetuba
4th rowitaquaquecetuba
5th rowibitinga
ValueCountFrequency (%)
sao 36194
 
17.9%
paulo 29438
 
14.6%
ibitinga 8242
 
4.1%
rio 5910
 
2.9%
do 5505
 
2.7%
preto 5497
 
2.7%
de 4181
 
2.1%
jose 4064
 
2.0%
santo 3214
 
1.6%
curitiba 3137
 
1.6%
Other values (640) 96723
47.9%
2024-08-05T15:35:58.640279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 197604
16.6%
o 145500
12.2%
i 101338
 
8.5%
84561
 
7.1%
r 77771
 
6.5%
s 75800
 
6.4%
e 63824
 
5.4%
u 62616
 
5.3%
p 58126
 
4.9%
l 56675
 
4.8%
Other values (31) 264393
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1102430
92.8%
Space Separator 84561
 
7.1%
Other Punctuation 612
 
0.1%
Modifier Symbol 369
 
< 0.1%
Dash Punctuation 164
 
< 0.1%
Close Punctuation 31
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Decimal Number 8
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 197604
17.9%
o 145500
13.2%
i 101338
9.2%
r 77771
 
7.1%
s 75800
 
6.9%
e 63824
 
5.8%
u 62616
 
5.7%
p 58126
 
5.3%
l 56675
 
5.1%
t 46959
 
4.3%
Other values (14) 216217
19.6%
Other Punctuation
ValueCountFrequency (%)
' 345
56.4%
/ 141
23.0%
. 76
 
12.4%
@ 38
 
6.2%
\ 6
 
1.0%
, 6
 
1.0%
Decimal Number
ValueCountFrequency (%)
4 2
25.0%
2 2
25.0%
5 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
84561
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 369
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1102430
92.8%
Common 85776
 
7.2%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 197604
17.9%
o 145500
13.2%
i 101338
9.2%
r 77771
 
7.1%
s 75800
 
6.9%
e 63824
 
5.8%
u 62616
 
5.7%
p 58126
 
5.3%
l 56675
 
5.1%
t 46959
 
4.3%
Other values (14) 216217
19.6%
Common
ValueCountFrequency (%)
84561
98.6%
´ 369
 
0.4%
' 345
 
0.4%
- 164
 
0.2%
/ 141
 
0.2%
. 76
 
0.1%
@ 38
 
< 0.1%
) 31
 
< 0.1%
( 31
 
< 0.1%
\ 6
 
< 0.1%
Other values (6) 14
 
< 0.1%
Inherited
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1187837
> 99.9%
None 369
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 197604
16.6%
o 145500
12.2%
i 101338
 
8.5%
84561
 
7.1%
r 77771
 
6.5%
s 75800
 
6.4%
e 63824
 
5.4%
u 62616
 
5.3%
p 58126
 
4.9%
l 56675
 
4.8%
Other values (29) 264022
22.2%
None
ValueCountFrequency (%)
´ 369
100.0%
Diacriticals
ValueCountFrequency (%)
̃ 2
100.0%

seller_state
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)< 0.1%
Missing830
Missing (%)0.7%
Memory size5.8 MiB
SP
83854 
MG
9260 
PR
9017 
RJ
 
5017
SC
 
4257
Other values (18)
 
6199

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters235208
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSC
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 83854
70.8%
MG 9260
 
7.8%
PR 9017
 
7.6%
RJ 5017
 
4.2%
SC 4257
 
3.6%
RS 2283
 
1.9%
DF 947
 
0.8%
BA 698
 
0.6%
GO 549
 
0.5%
PE 465
 
0.4%
Other values (13) 1257
 
1.1%
(Missing) 830
 
0.7%

Length

2024-08-05T15:35:58.801542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 83854
71.3%
mg 9260
 
7.9%
pr 9017
 
7.7%
rj 5017
 
4.3%
sc 4257
 
3.6%
rs 2283
 
1.9%
df 947
 
0.8%
ba 698
 
0.6%
go 549
 
0.5%
pe 465
 
0.4%
Other values (13) 1257
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 93400
39.7%
S 90854
38.6%
R 16387
 
7.0%
M 9879
 
4.2%
G 9809
 
4.2%
J 5017
 
2.1%
C 4361
 
1.9%
A 1119
 
0.5%
E 968
 
0.4%
D 947
 
0.4%
Other values (6) 2467
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 235208
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 93400
39.7%
S 90854
38.6%
R 16387
 
7.0%
M 9879
 
4.2%
G 9809
 
4.2%
J 5017
 
2.1%
C 4361
 
1.9%
A 1119
 
0.5%
E 968
 
0.4%
D 947
 
0.4%
Other values (6) 2467
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 235208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 93400
39.7%
S 90854
38.6%
R 16387
 
7.0%
M 9879
 
4.2%
G 9809
 
4.2%
J 5017
 
2.1%
C 4361
 
1.9%
A 1119
 
0.5%
E 968
 
0.4%
D 947
 
0.4%
Other values (6) 2467
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 93400
39.7%
S 90854
38.6%
R 16387
 
7.0%
M 9879
 
4.2%
G 9809
 
4.2%
J 5017
 
2.1%
C 4361
 
1.9%
A 1119
 
0.5%
E 968
 
0.4%
D 947
 
0.4%
Other values (6) 2467
 
1.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2018
63924 
2017
54105 
2016
 
405

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters473736
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 63924
54.0%
2017 54105
45.7%
2016 405
 
0.3%

Length

2024-08-05T15:35:58.924009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-05T15:35:59.055421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 63924
54.0%
2017 54105
45.7%
2016 405
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 118434
25.0%
0 118434
25.0%
1 118434
25.0%
8 63924
13.5%
7 54105
11.4%
6 405
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 473736
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 118434
25.0%
0 118434
25.0%
1 118434
25.0%
8 63924
13.5%
7 54105
11.4%
6 405
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 473736
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 118434
25.0%
0 118434
25.0%
1 118434
25.0%
8 63924
13.5%
7 54105
11.4%
6 405
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 118434
25.0%
0 118434
25.0%
1 118434
25.0%
8 63924
13.5%
7 54105
11.4%
6 405
 
0.1%

order_purchase_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0337234
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:59.177090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2318054
Coefficient of variation (CV)0.53562372
Kurtosis-0.98261973
Mean6.0337234
Median Absolute Deviation (MAD)2
Skewness0.20436226
Sum714598
Variance10.444566
MonotonicityNot monotonic
2024-08-05T15:35:59.297979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 12745
10.8%
5 12684
10.7%
7 12253
10.3%
3 11790
10.0%
6 11209
9.5%
4 11135
9.4%
2 10068
8.5%
1 9614
8.1%
11 9111
7.7%
12 6617
5.6%
Other values (2) 11208
9.5%
ValueCountFrequency (%)
1 9614
8.1%
2 10068
8.5%
3 11790
10.0%
4 11135
9.4%
5 12684
10.7%
6 11209
9.5%
7 12253
10.3%
8 12745
10.8%
9 5172
4.4%
10 6036
5.1%
ValueCountFrequency (%)
12 6617
5.6%
11 9111
7.7%
10 6036
5.1%
9 5172
4.4%
8 12745
10.8%
7 12253
10.3%
6 11209
9.5%
5 12684
10.7%
4 11135
9.4%
3 11790
10.0%

order_purchase_day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.547393
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:59.438771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6680764
Coefficient of variation (CV)0.55752602
Kurtosis-1.1787215
Mean15.547393
Median Absolute Deviation (MAD)8
Skewness0.017350748
Sum1841340
Variance75.135549
MonotonicityNot monotonic
2024-08-05T15:35:59.575455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
24 4600
 
3.9%
16 4286
 
3.6%
15 4164
 
3.5%
5 4148
 
3.5%
6 4120
 
3.5%
18 4073
 
3.4%
4 4034
 
3.4%
19 4030
 
3.4%
7 4021
 
3.4%
26 3983
 
3.4%
Other values (21) 76975
65.0%
ValueCountFrequency (%)
1 3680
3.1%
2 3844
3.2%
3 3824
3.2%
4 4034
3.4%
5 4148
3.5%
6 4120
3.5%
7 4021
3.4%
8 3982
3.4%
9 3863
3.3%
10 3731
3.2%
ValueCountFrequency (%)
31 1986
1.7%
30 3021
2.6%
29 3095
2.6%
28 3558
3.0%
27 3810
3.2%
26 3983
3.4%
25 3937
3.3%
24 4600
3.9%
23 3799
3.2%
22 3825
3.2%

order_purchase_hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.75605
Minimum0
Maximum23
Zeros2910
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-08-05T15:35:59.725442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median15
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.3272401
Coefficient of variation (CV)0.36102075
Kurtosis0.19316826
Mean14.75605
Median Absolute Deviation (MAD)4
Skewness-0.60239495
Sum1747618
Variance28.379487
MonotonicityNot monotonic
2024-08-05T15:35:59.859941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16 8039
 
6.8%
14 7944
 
6.7%
11 7821
 
6.6%
13 7729
 
6.5%
15 7669
 
6.5%
10 7392
 
6.2%
17 7302
 
6.2%
20 7298
 
6.2%
21 7290
 
6.2%
12 7236
 
6.1%
Other values (14) 42714
36.1%
ValueCountFrequency (%)
0 2910
2.5%
1 1369
 
1.2%
2 616
 
0.5%
3 326
 
0.3%
4 254
 
0.2%
5 226
 
0.2%
6 571
 
0.5%
7 1428
 
1.2%
8 3523
3.0%
9 5694
4.8%
ValueCountFrequency (%)
23 4909
4.1%
22 6947
5.9%
21 7290
6.2%
20 7298
6.2%
19 7043
5.9%
18 6898
5.8%
17 7302
6.2%
16 8039
6.8%
15 7669
6.5%
14 7944
6.7%

Interactions

2024-08-05T15:35:24.917135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:17.535266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:21.061828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:31.156379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:34.321949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:39.177393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:43.509984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:35:10.299368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:14.683578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:19.702186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:58.868242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:03.491532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:18.306877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:25.240744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:40.278076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:47.506829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:35:25.977520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:18.496156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:22.023482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:34:51.081281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:35:04.289658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:35:11.094442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:16.377184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:20.515258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:26.810945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:18.674424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:22.207123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:25.615942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:29.088206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:32.190798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:35.657940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:41.224093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:44.586139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-08-05T15:35:33.426905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:19.814145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:23.175968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:26.578637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:29.988755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:33.091515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:37.027987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:42.264364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:45.528061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:48.847748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:52.272198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:57.002309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:01.067893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:05.515852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:08.826854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:12.406984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:17.671601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:23.169518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:33.801853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:19.993928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:23.346147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:26.746847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:30.146755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:33.260314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:37.258589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:42.440762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:45.707726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:49.023709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:52.454464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:57.269843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:01.305310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:05.684302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:09.011133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:12.608181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:17.931143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:23.484981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:34.080372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:20.164385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:23.538527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:26.928210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:30.309978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:33.432692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:37.528683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:42.619884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:45.886729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:49.221895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:52.634618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:57.478129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:01.544657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:05.875929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:09.186706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:12.819693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:18.268877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:23.733192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:34.377823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:20.336666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:23.731652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:27.116022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:30.472980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:33.598659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:37.879591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:42.803920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:46.058167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:49.405358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:52.810197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:57.695494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:01.797578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:06.069131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:09.362434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:13.009951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:18.594394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:24.042948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:34.658649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:20.509763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:23.915935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:27.308057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:30.640940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:33.774905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:38.238854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:42.987175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:46.233129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:49.599248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:52.992197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:57.890828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:02.095312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:06.243325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:09.550884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:13.268947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:18.841346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:24.297781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:34.988693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:20.688310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:24.094081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:27.489615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:30.816100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:33.957130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:38.614879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:43.164645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:46.410279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:49.781708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:53.191199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:58.078142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:02.363473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:06.423575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:09.731636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:13.518619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:19.059906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:24.517767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:35.302618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:20.888666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:24.275357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:27.670832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:30.993161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:34.132622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:38.936092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:43.338829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:46.586391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:49.970779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:53.399160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:34:58.272865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:02.644295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:06.607895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:09.916185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:13.817703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:19.288095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-08-05T15:35:24.723169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-08-05T15:36:00.014304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_statecustomer_zip_code_prefixfreight_valueorder_item_idorder_purchase_dayorder_purchase_hourorder_purchase_monthorder_purchase_yearorder_statuspayment_installmentspayment_sequentialpayment_typepayment_valuepriceproduct_description_lenghtproduct_height_cmproduct_length_cmproduct_name_lenghtproduct_photos_qtyproduct_weight_gproduct_width_cmseller_stateseller_zip_code_prefix
customer_state1.0000.8960.0850.0130.0110.0200.0210.0420.0260.0320.0260.0330.0290.0200.0270.0190.0170.0130.0140.0280.0160.0540.069
customer_zip_code_prefix0.8961.0000.466-0.009-0.0100.012-0.0070.0360.0220.069-0.0080.0290.1060.0700.0310.0190.0090.0150.0260.026-0.0020.0650.060
freight_value0.0850.4661.000-0.055-0.0170.007-0.0030.0280.0150.1910.0180.0090.4230.4340.1170.2850.2840.0330.0110.4490.2760.0480.257
order_item_id0.013-0.009-0.0551.0000.014-0.0150.0010.0070.0020.060-0.0070.0210.256-0.116-0.0310.0180.006-0.021-0.064-0.000-0.0040.000-0.012
order_purchase_day0.011-0.010-0.0170.0141.000-0.013-0.0020.0830.010-0.001-0.0030.007-0.009-0.021-0.004-0.004-0.0090.0090.000-0.007-0.0020.015-0.004
order_purchase_hour0.0200.0120.007-0.015-0.0131.000-0.0020.0180.0100.0300.0130.037-0.0060.009-0.0090.0020.0090.0010.0050.0160.0110.010-0.004
order_purchase_month0.021-0.007-0.0030.001-0.002-0.0021.0000.4610.0240.0270.0010.0300.0010.002-0.0180.0200.0020.0140.0260.0090.0150.0390.009
order_purchase_year0.0420.0360.0280.0070.0830.0180.4611.0000.0670.0480.0300.0410.0080.0000.0490.0410.0500.0820.0290.0150.0470.0840.065
order_status0.0260.0220.0150.0020.0100.0100.0240.0671.0000.0050.0260.0370.0150.0140.0160.0150.0140.0190.0130.0110.0040.0290.013
payment_installments0.0320.0690.1910.060-0.0010.0300.0270.0480.0051.000-0.1770.2360.3960.3170.0330.1060.1080.016-0.0020.1980.1240.0330.066
payment_sequential0.026-0.0080.018-0.007-0.0030.0130.0010.0300.026-0.1771.0000.198-0.214-0.006-0.0130.0130.034-0.003-0.0050.0300.0280.0160.007
payment_type0.0330.0290.0090.0210.0070.0370.0300.0410.0370.2360.1981.0000.0180.0150.0200.0150.0220.0100.0040.0180.0200.0210.018
payment_value0.0290.1060.4230.256-0.009-0.0060.0010.0080.0150.396-0.2140.0181.0000.7900.1690.3050.2280.024-0.0110.4490.2320.0360.160
price0.0200.0700.434-0.116-0.0210.0090.0020.0000.0140.317-0.0060.0150.7901.0000.2110.3280.2660.0420.0290.5140.2710.0520.176
product_description_lenght0.0270.0310.117-0.031-0.004-0.009-0.0180.0490.0160.033-0.0130.0200.1690.2111.0000.134-0.0200.0730.1100.095-0.0800.1120.002
product_height_cm0.0190.0190.2850.018-0.0040.0020.0200.0410.0150.1060.0130.0150.3050.3280.1341.0000.250-0.057-0.0790.5330.3400.0650.050
product_length_cm0.0170.0090.2840.006-0.0090.0090.0020.0500.0140.1080.0340.0220.2280.266-0.0200.2501.0000.0600.0060.6190.6320.0840.067
product_name_lenght0.0130.0150.033-0.0210.0090.0010.0140.0820.0190.016-0.0030.0100.0240.0420.073-0.0570.0601.0000.1630.0750.0650.0700.009
product_photos_qty0.0140.0260.011-0.0640.0000.0050.0260.0290.013-0.002-0.0050.004-0.0110.0290.110-0.0790.0060.1631.0000.004-0.0140.040-0.078
product_weight_g0.0280.0260.449-0.000-0.0070.0160.0090.0150.0110.1980.0300.0180.4490.5140.0950.5330.6190.0750.0041.0000.6210.0780.097
product_width_cm0.016-0.0020.276-0.004-0.0020.0110.0150.0470.0040.1240.0280.0200.2320.271-0.0800.3400.6320.065-0.0140.6211.0000.0570.078
seller_state0.0540.0650.0480.0000.0150.0100.0390.0840.0290.0330.0160.0210.0360.0520.1120.0650.0840.0700.0400.0780.0571.0000.920
seller_zip_code_prefix0.0690.0600.257-0.012-0.004-0.0040.0090.0650.0130.0660.0070.0180.1600.1760.0020.0500.0670.009-0.0780.0970.0780.9201.000

Missing values

2024-08-05T15:35:36.418651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-05T15:35:38.659680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-05T15:35:40.755633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idcustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateorder_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_dateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valuepayment_sequentialpayment_typepayment_installmentspayment_valueproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_categoryseller_zip_code_prefixseller_cityseller_stateorder_purchase_yearorder_purchase_monthorder_purchase_dayorder_purchase_hour
006b8999e2fba1a1fbc88172c00ba8bc7861eff4711a542e4b93843c6dd7febb014409francaSP00e7ee1b050b8499577073aeb2a297a1delivered2017-05-16 15:05:352017-05-16 15:22:122017-05-23 10:47:572017-05-25 10:35:352017-06-05 00:00:001.0a9516a079e37a9c9c36b9b78b10169e87c67e1448b00f6e969d365cea6b010ab2017-05-22 15:22:12124.9921.881.0credit_card2.0146.8741.01141.01.08683.054.064.031.0office_furniture8577.0itaquaquecetubaSP201751615
118955e83d337fd6b2def6b18a428ac77290c77bc529b7ac935b93aa66c333dc39790sao bernardo do campoSP29150127e6685892b6eab3eec79f59c7delivered2018-01-12 20:48:242018-01-12 20:58:322018-01-15 17:14:592018-01-29 12:41:192018-02-06 00:00:001.04aa6014eceb682077f9dc4bffebc05b0b8bc237ba3788b23da09c0f1f3a3288c2018-01-18 20:58:32289.0046.481.0credit_card8.0335.4843.01002.03.010150.089.015.040.0housewares88303.0itajaiSC201811220
24e7b3e00288586ebd08712fdd0374a03060e732b5b29e8181a18229c7b0b2b5e1151sao pauloSPb2059ed67ce144a36e2aa97d2c9e9ad2delivered2018-05-19 16:07:452018-05-20 16:19:102018-06-11 14:31:002018-06-14 17:58:512018-06-13 00:00:001.0bd07b66896d6f1494f5b86251848ced77c67e1448b00f6e969d365cea6b010ab2018-06-05 16:19:10139.9417.791.0credit_card7.0157.7355.0955.01.08267.052.052.017.0office_furniture8577.0itaquaquecetubaSP201851916
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